Cannon, neural network, physical model

In my weekly meeting with Teresa Huang (JHU) and Soledad Villar (JHU), we went through our methods for putting labels on stellar spectra (labels like effective temperature, surface gravity, and metallicity). We have all the machinery together now to do this with physical models, with The Cannon (a data-driven generative model), and with neural networks (deep learning, or other data-driven discriminative models). The idea is to see how well these different kinds of models respect our beliefs about stars and spectroscopic observations, and how they fit or over-fit, as a function of training and model choices. We are using the concept of adversarial attacks to guide us. All our pieces are in place now to do this full set of comparisons.